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机构地区:[1]College of Hydropower and Information Engineering,Huazhong University of Science and Technology,Wuhan,430074,P.R.China [2]School of Electric Power Engineering,Kunming University of Science and Technology,Kunming,650051,P.R.Ching.
出 处:《Journal of Donghua University(English Edition)》2002年第4期111-114,共4页东华大学学报(英文版)
基 金:Yunnan Natural Science Foundation under contract 98E004Z
摘 要:Stochastic dynamic programming (SDP) is extensively used in the optimization for long-term reservoir operations. Generally, both of the steady state optimal policy and its associated performance indices (PIs) for multipurpose reservoir are of prime importance. To derive the PIs there are two typical ways: simulation and probability formula. Among the disadvantages, one is that these approaches require the pre-specified operation policy. IHuminated by the convergence of objective function in SDP, a new approach, which has the advantage that its use can be concomitant with the solving of SDP, is proposed to determine the desired PIs. In the case study, its efficiency is also practically tested.Stochastic dynamic programming (SDP) is extensively used in the optimization for long-term reservoir operations. Generally, both of the steady state optimal policy and its associated performance indices (PIs) for multipurpose reservoir are of prime importance. To derive the PIs there are two typical ways: simulation and probability formula. Among the disadvantages, one is that these approaches require the pre-specified operation policy. Illuminated by the convergence of objective function in SDP, a new approach, which has the advantage that its use can be concomitant with the solving of SDP, is proposed to determine the desired PIs. In the case study, its efficiency is also practically tested.
关 键 词:DYNAMIC conv ergence performance index stochastic DYNAMIC programming RESERVOIR operation Markovian characteristic.
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